Download presentation

Presentation is loading. Please wait.

1
**Dyadic Analysis: Using HLM**

David A. Kenny University of Connecticut /

2
**Types of Dyads Definitions Distinguishable**

Dyads with a categorical within-dyads variables that makes a difference e. g., parent-child Indistinguishable Ordering of the two members is arbitrary e.g. roommates Whether dyads are distinguishable or not is matter of theoretical and statistical considerations.

3
**Nonindependence: Definition**

Degree of greater similarity (or dissimilarity) between linked observations versus unlinked observations Nonindependence as the correlation between linked observations.

4
**Negative Nonindependence**

Two scores from the same dyad are more dissimilar than two scores from different groups. How?

5
Compensation: If one person has a large score, the other person lowers his or her score. For example, if one person acts very friendly, the partner may distance him or herself, Social comparison: The members of the dyad use the relative difference on some measure to determine some other variable. For instance, satisfaction after a tennis match is determined by the score of that match. Zero-sum: The sum of two scores is the same for each dyad. For instance, the two members divide a reward that is the same for all dyads. Division of labor: Dyad members assign one member to do one task and the other member to do another. For instance, the amount of housework done in the household may be negatively correlated.

6
**Dyadic Designs Standard Each person has one partner**

Social Relations Model (SRM) Designs Each person has many partners, and each partner paired with many persons One-with-Many Each person has many partners, but each partner is paired with only one person

7
Standard Design

8
* One-with-Many Design *

9
* SRM Designs *

10
Remaining part of the presentation focuses on the standard design which is used in most dyadic studies.

11
**Dyadic Data Organization**

Individual One record for each individual Only that individual’s data on the record Dyad (useful for distinguishable dyads) Each record one dyad Different variables for each person Pairwise (useful for distinguishable dyads) One record for each person The person’s data and partner data included (each data point included twice)

12
**Pairwise Data Organization**

Dyad Number Member Number The Person’s Data Partner’s Data Acitelli Example

13
**Types of Variables Between Dyads – Level Two Within Dyads**

Both members have the same score X = X′ Within Dyads Sum of two scores a constant X + X′ = c for all dyads

14
Between

15
Within

16
**Mixed Variables Variables that vary within and between groups Examples**

Most outcomes (and mediators) Individual differences (e.g., personality)

17
Mixed

18
**Gender and the Three Types**

Between Gay and Lesbian Couples Within Heterosexual Married Couples Mixed Friends

19
**Actor-Partner Interdependence Model**

X Y partner partner actor X' Y'

20
**Types of APIM Models actor only a > 0; p = 0 partner only**

couple model a = p social comparison model a + p = 0

21
**Actor-Partner Interaction**

Partner effect depends on the level of the actor effect More than one way to test A Level 2 Variable

22
**Measurement of the Actor-Partner Interaction**

Standard Product Approach (XX’) – make sure variables centered Discrepancy Score (│X – X′│) (Absolute Difference) Similarity – Negative Coefficient Dissimilarity – Positive Coefficient Higher Score of the Two [Max(X,X′)] – “It Takes Two” Lower Score [Min(X,X′)] – “One of Us Can Drag Us Both Down”

23
**Estimation Indistinguishable Members Multilevel Modeling**

Pairwise Data File Illustrate with HLM Distinguishable Members Structural Equation Modeling Dyad Data File

24
**Approaches Illustrated**

Conventional Multilevel Model with Random Intercepts Two-Intercept Model

25
**Briefly Discussed One-with-many design**

Standard design studied longitudinally

Similar presentations

OK

Overview of Regression Analysis. Conditional Mean We all know what a mean or average is. E.g. The mean annual earnings for 25-44 year old working males.

Overview of Regression Analysis. Conditional Mean We all know what a mean or average is. E.g. The mean annual earnings for 25-44 year old working males.

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on area of parallelogram vectors Ppt on hindu religion video Ppt on main idea Ppt on excess demand and deficient demand Ppt on death penalty in india Ppt on culture of assam Ppt online examination project Ppt on north indian cuisine Ppt on diode as rectifier circuit Ppt on tsunami warning system to mobile